A Meta-Analysis of Scaffolding Effects in Online Learning in Higher Education
Bibliographic record
Abstract
The significance of scaffolding in education has received considerable attention. Many studies have examined the effects of scaffolding with diverse groups of participants, purposes, learning outcomes, and learning environments. The purpose of this research was to conduct a meta-analysis of the effects of scaffolding on learning outcomes in an online learning environment in higher education. This meta-analysis included studies with 64 effect sizes from 18 journal articles published in English, in eight countries, from 2010 to 2019. The meta-analysis revealed that scaffolding in an online learning environment has a large and statistically significant effect on learning outcomes. The meta-cognitive domain yielded a larger effect size than did the affective and cognitive domains. In terms of types of scaffolding activities, meta-cognitive scaffolding outnumbered other types of scaffolding. Computers as a scaffolding source in an online learning environment were also more prevalent than were human instructors. In addition, scholars in the United States have produced a large portion of the scaffolding research. Finally, the academic area of language and literature has adopted scaffolding most widely. Given that effective scaffolding can improve the quality of learning in an online environment, the current research is expected to contribute to online learning outcomes and learning experiences.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.043 | 0.103 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.045 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".